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测量误差情况下的有效插补方法。

Efficient imputation methods in case of measurement errors.

作者信息

Kumar Anoop, Bhushan Shashi, Shukla Shivam, Bakr M E, Alshangiti Arwa M, Balogun Oluwafemi Samson

机构信息

Department of Statistics, Central University of Haryana, Mahendergarh, 123031, India.

Department of Statistics, University of Lucknow, Lucknow, 226007, India.

出版信息

Heliyon. 2024 Feb 28;10(6):e26864. doi: 10.1016/j.heliyon.2024.e26864. eCollection 2024 Mar 30.

Abstract

This manuscript develops few efficient difference and ratio kinds of imputations to handle the situation of missing observations given that these observations are polluted by the measurement errors (ME). The mean square errors of the developed imputations are studied to the primary degree approximation by adopting Taylor series expansion. The proposed imputations are equated with the latest existing imputations presented in the literature. The execution of the proposed imputations is assessed by utilizing a broad empirical study utilizing some real and hypothetically created populations. Appropriate remarks are made for sampling respondents regarding practical applications.

摘要

本手稿提出了几种有效的差分和比率类型的插补方法,以处理观测值缺失且这些观测值受到测量误差(ME)污染的情况。通过采用泰勒级数展开,对所提出的插补方法的均方误差进行了一阶近似研究。将所提出的插补方法与文献中最新提出的现有插补方法进行了比较。通过利用一些真实和假设生成的总体进行广泛的实证研究,对所提出的插补方法的执行情况进行了评估。针对实际应用中的抽样受访者给出了适当的说明。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57ff/10950506/3d9e2b66c10e/gr001.jpg

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